Drug Repurposing for Parkinson’s Disease: A Large-Scale Multi-Cohort Study

Drug Repurposing for Parkinson’s Disease: A Large-Scale Multi-Cohort Study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Drug Repurposing for Parkinson’s Disease: A Large-Scale Multi-Cohort Study View ORCID Profile Yuxuan Hu , View ORCID Profile Weiqiang Liu , Miles Waits , View ORCID Profile Yiyong Zhao , View ORCID Profile Maria I. Olivero-Acosta , View ORCID Profile Le Zhang , View ORCID Profile Clemens R. Scherzer , View ORCID Profile Xianjun Dong doi: https://doi.org/10.1101/2025.05.20.25327943 Yuxuan Hu 1 Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine , New Haven, CT 06510, USA 2 Department of Neurology, Yale School of Medicine , New Haven, CT 06510, USA 3 Jiangsu Key Laboratory of Bioactive Natural Product Research and State Key Laboratory of Natural Medicines, China Pharmaceutical University , Nanjing 211100, China 4 Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School , Boston, MA 02115, USA 5 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA BSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yuxuan Hu Weiqiang Liu 1 Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine , New Haven, CT 06510, USA 2 Department of Neurology, Yale School of Medicine , New Haven, CT 06510, USA 5 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Weiqiang Liu Miles Waits 4 Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School , Boston, MA 02115, USA BSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yiyong Zhao 1 Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine , New Haven, CT 06510, USA 2 Department of Neurology, Yale School of Medicine , New Haven, CT 06510, USA 5 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yiyong Zhao Maria I. Olivero-Acosta 1 Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine , New Haven, CT 06510, USA 2 Department of Neurology, Yale School of Medicine , New Haven, CT 06510, USA 5 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maria I. Olivero-Acosta Le Zhang 2 Department of Neurology, Yale School of Medicine , New Haven, CT 06510, USA 5 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA 6 Department of Neuroscience, Yale School of Medicine , New Haven, CT 06510, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Le Zhang Clemens R. Scherzer 1 Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine , New Haven, CT 06510, USA 2 Department of Neurology, Yale School of Medicine , New Haven, CT 06510, USA 5 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA 6 Department of Neuroscience, Yale School of Medicine , New Haven, CT 06510, USA 7 Department of Genetics, Yale School of Medicine , New Haven, CT 06510, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Clemens R. Scherzer Xianjun Dong 1 Stephen & Denise Adams Center for Parkinson’s Disease Research of Yale School of Medicine , New Haven, CT 06510, USA 2 Department of Neurology, Yale School of Medicine , New Haven, CT 06510, USA 4 Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School , Boston, MA 02115, USA 5 Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network , Chevy Chase, MD 20815, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xianjun Dong For correspondence: xianjun.dong{at}yale.edu Abstract Full Text Info/History Metrics Data/Code Preview PDF Summary Background Progress in Parkinson’s disease (PD) is hampered by two critical obstacles: the lack of disease-modifying drugs and the difficulty of identify individuals at early, prodromal disease stages for clinical trials. Methods We conducted a retrospective, multi-cohort study using data from the Mass General Brigham (MGB) Biobank for discovery and the Accelerating Medicines Partnership Parkinson’s Disease (AMP-PD) program for replication. Logistic regression was used to estimate the associations between drug exposure and the subsequent risk of PD. Sensitivity analyses addressed dose effects and reverse causality. Longitudinal changes in cognitive and motor function were analyzed using linear mixed-effects models. We also mapped drug target genes to cell types to explore potential therapeutic targets for PD. Findings We included a total of 44,388 individuals in the discovery cohort (15,032 cases and 29,356 controls) and 4010 individuals in the validation cohort (2,351 cases and 1659 controls). In this study, 18 drugs were replicated, with five associated with reduced PD risk and 13 with increased risk. Notably, salbutamol (discovery: 0.84, 95% CI 0.80–0.88; replication: 0.70, 95% CI 0.49–0.99) and losartan prescriptions (discovery: 0.90, 95% CI 0.84–0.96; replication: 0.67, 95% CI 0.51–0.88) were associated with reduced risk of PD with lag times of 0-5 years. Moreover, in longitudinal analyses of patients with PD over 10 years, cognitive function over time was improved in patients taking losartan compared to patients not on this drug. Solifenacin was associated with less severe motor impairment. Furthermore, eight differentially expressed drug target genes were identified in PD, including significant downregulation of CFTR in oligodendrocytes and NR3C2 in microglia. Interpretation Salbutamol and losartan show significant associations with PD risk and can be further tested mechanistically and clinically. Drugs for non-motor symptoms show promise for identifying putative prodromal PD at scale using data science. CFTR and NR3C2 represent compelling druggable target candidates for therapeutic development in PD and warrant further functional validation. Funding The Michael J. Fox Foundation for Parkinson’s Research (MJFF) and the Aligning Science Across Parkinson’s (ASAP) initiative. Introduction Parkinson’s disease (PD) is a common neurodegenerative disease associated with movement abnormality, estimated to affect 13 million people by 2040. 1 , 2 The annual expenses for PD have recently been estimated at $52 billion in the US alone. 3 Current pharmacological treatments primarily focus on symptom management through dopamine restoration, with Levodopa remaining the cornerstone therapy. 4 However, Carbidopa-Levodopa treatment is associated with significant side effects, highlighting the urgent need for alternative therapeutics. Moreover, as a multifactorial genetic disorder, the precise mechanisms underlying the pathogenesis of PD remain unclear, presenting tremendous challenges for drug development. 5 – 8 Drug repurposing has emerged as a promising strategy to accelerate drug discovery for PD. 9 Several epidemiological studies have investigated the association between medication use and PD risk, uncovering potential candidates for drug repurposing. 10 – 14 Romanowska et al . identified 31 drug classes linked to PD risk using longitudinal data from the Norwegian Prescription Registry. 12 However, many of these studies have been limited by small sample sizes, a lack of independent replication and biological insights, or a narrow focus on specific drug classes, underscoring the urgent need for a more comprehensive and systematic investigation. To address these gaps, we conducted a large-scale, multi-cohort study to systematically evaluate the associations between drug use and subsequent PD risk, leveraging data from the Mass General Brigham (MGB) Biobank for discovery and the Accelerating Medicines Partnership Parkinson’s Disease (AMP-PD) program for replication. Additionally, we examined the impact of drug use on PD progression, specifically cognitive and motor impairment, to identify potential treatments that slow or exacerbate PD progression. Lastly, we performed a gene-level drug-disease association analysis, integrating transcriptomic data to assess the biological plausibility of candidate drugs, providing insights into potential therapeutic targets and personalized treatment strategies for PD ( figure 1A ). Download figure Open in new tab Figure 1. Drug Repurposing for Parkinson’s Disease. (A) A schema of drug repurposing workflow for PD. (B) A forest plot of five drugs that were significantly associated with reduced PD risk. (C) A Sankey plot of 15 repurposed drugs by replicating ATC codes. MGB: Mass General Brigham; PPMI: Parkinson’s Progression Markers Initiative; PDBP: Parkinson’s Disease Biomarkers Program; PD: Parkinson’s Disease; AMP-PD: Accelerating Medicines Partnership Parkinson’s Disease; aOR: Adjusted Odds Ratio; CI: Confidence Interval. Methods Study Design and Participants We conducted a retrospective multi-cohort study, utilizing all available participants from the MGB Biobank as the discovery cohort and the AMP-PD program as the replication cohort. The MGB Biobank is a large-scale research data and sample repository at MGB that has enrolled more than 145,000 patients. 15 , 16 For the discovery cohort, we identified all patients diagnosed with PD (ICD-10 code: G20) from the MGB Biobank database and matched each case with two non-PD controls based on age and sex (ratio 1:2), forming the final dataset for analysis. To adjust for potential confounders and enhance comparability between PD cases and control cases, propensity score matching (PSM) 17 was performed based on birth date, age, sex, and race, thereby reducing selection bias. The AMP-PD program, 18 launched in 2018, is a collaborative initiative aimed at identifying novel therapeutic targets and developing biomarkers for PD. In this study, two largest AMP-PD sub-cohorts with more than 1,000 participants (PPMI and PDBP) were integrated to forge the replication cohort. PD cases and controls were defined according to standardized AMP-PD criteria. We obtained demographic data from both cohorts, including age, sex, race, and birth year. Each participant was assigned a unique identification code to facilitate data linkage across sources while ensuring privacy protection. Additionally, all disease-related diagnosis records from the MGB Biobank were collected. Prescription medication data, including month and year of prescription, drug name and provider details, were available for the MGB Biobank and PPMI cohorts. However, the PDBP cohort did not contain exact prescription dates; instead, medication history was recorded relative to the baseline visit, indicating whether the medication was initiated before or after enrollment. To ensure data quality and comparability, we applied consistent inclusion and exclusion criteria for participants across both cohorts ( appendix p 2 ). To further evaluate the associations between the use of drugs and subsequent PD progression of cognitive and motor impairment, we collected all the information required for this study from the PPMI database and PDBP database. All the PD cases that met the above criteria for the PD case group were included in this study. We excluded the following participants: (1) those who had no longitudinal data of cognition or motor impairment at baseline, and (2) those who were lost to follow-up within 3 years after enrollment. Procedures In the discovery cohort, PD cases were identified based on the codes defined in the appendix (p 5) . The index date for PD cases was determined as the date of first recorded PD diagnosis in the diagnosis history records, while for controls, the index date was set as the date of data download. In the replication cohort, PD cases were defined according to the standardized criteria of the AMP-PD program, which employs rigorous clinical assessments, making the PD diagnosis more stringent compared to the discovery cohort. Both PD and control subjects in the replication cohort were assigned the enrollment date as the index date. Moreover, the detailed descriptions of cognitive and motor function assessments, including baseline thresholds and longitudinal outcome definitions, are provided in the appendix (p 2) . Medications were standardized using the DrugBank 19 and PubChem 20 databases to ensure consistency for analysis ( appendix p 5 ). In the discovery cohort, derived from electronic health records (EHRs), drug names were extracted from the medication field according to system-specific formatting and mapped to their corresponding generic names using database synonyms. In the replication cohort, where records were manually entered, spelling errors were systematically reviewed and corrected before applying the same standardization process. Notably, combination therapies recorded as a single entry were split into separate records to accurately reflect individual active ingredients. Individuals who had at least one prescription record of any drug were considered exposed to this drug. Prescriptions after the index date were excluded from the analysis. To minimize unreliable results, we restricted the analyses to the drugs for which there were at least 10 patients who had taken these drugs. We used Anatomical Therapeutic Chemical (ATC) codes to identify the categories of drugs. Statistical analysis All statistical analyses were performed using R (version 4.4.1, R Project for Statistical Computing) and all scripts can be found on Zenodo (10.5281/zenodo.15360497). For the primary analyses, we conducted separate logistic regression analyses for all the medications in each cohort. Specifically, for drugs used by at least 10 patients within a cohort, a logistic regression model was employed to estimate the association between drug exposure and the risk of developing PD. The odds ratio (OR) and corresponding 95% confidence interval (CI) were calculated for each drug, with statistical significance assessed via p-values. To reduce the false positives, we applied the Benjamini-Hochberg method-based false discovery rate (FDR) correction across all drugs in the discovery cohort, thereby enhancing the reliability of our findings. All models were adjusted for age, sex, and race to control for potential confounding factors ( appendix p 5 ). In the replication cohort, models were also adjusted for source (PPMI or PDBP). Participants with missing data in any of these covariates were excluded from the analysis to maintain data integrity. PSM was performed to balance the covariates between the PD cases and controls in the discovery cohort. Nearest neighbor matching with a 1:2 ratio and a maximum caliper of 0.1 was used to ensure a covariate balance between the two groups. The propensity score model was specified with the following covariates: birth year, age, sex, and race. We obtained two final lists of replicated drugs, defined as those identified in both the discovery and replication cohorts using two replication methods ( appendix p 2 ). The sensitivity analyses were performed in the discovery cohort on drug exposure definition and time window prior to the index date, respectively ( appendix p 2 ). We applied linear mixed-effects models (LMMs) to assess the associations of drug use before the index date and longitudinal changes in Montreal Cognitive Assessment (MoCA) scores and Unified Parkinson’s Disease Rating Scale Part (UPDRS) III scores. The primary independent variable, drug use before the index date, was treated as a binary variable, and time was modeled as a continuous variable representing follow-up duration. The models of longitudinal changes in MoCA scores and UPDRS III scores included an interaction term with time effect and drug use before the index date to evaluate whether it was associated with different rates of PD progression on cognitive and motor impairment over time. The covariates were baseline age, sex, race, education level, and source. The measurement of interest was the interaction between the use of drugs and time. A random intercept for each participant was included to account for within-subject variability. The FDR values less than 0.1 were regarded as statistically significant. We collected protein target genes of identified drugs from the Connectivity Map (CMap) database. 21 The identified drugs contained drugs replicated by ATC codes that were associated with reduced PD risk. Each drug was queried using the Touchstone tool of CMap to obtain its protein target genes and mechanisms of action (MoA). Single-nucleus RNA sequencing data was processed using Seurat (version 5.1.0). Quality control, clustering, and cell type annotation were conducted following the same procedures as described in Zhu et al . 22 To generate a pseudo-bulk gene expression matrix, we applied the ‘AggregateExpression’ function in Seurat, grouping data by sample and cell type. Differential expression analysis was performed using DESeq2 (version 1.46.0) in R, incorporating sex, age, and post-mortem interval (PMI) as covariates. To mitigate the potential influence of age and PMI due to differences in scale, these variables were standardized using the ‘scale (center = TRUE, scale = TRUE)’ function in R. Genes were considered differentially expressed if p value < 0.05. For visualization, gene expression levels were normalized using counts per million (CPM). STRING database was applied to analyze the protein-protein interaction network. 23 Role of the funding source The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Results Study Cohorts and Participant Characteristics In the initial data acquisition, we identified a total of 92,141 individuals (discovery: 86,808; replication: 5,333). After applying rigorous inclusion and exclusion criteria, 49,736 individuals remained for analysis (discovery: 45,726, including 15,047 PD cases; replication: 4,010, including 2,351 PD cases). A detailed inclusion and exclusion process is outlined in the appendix (p 30) . Following PSM, 44,388 individuals were included in the discovery cohort, with 15,032 PD cases ( appendix pp 6 , 31 ). Among these cases, 93.2% were aged over 60 years, 59.1% were male, and 87.8% were recorded as White. In the replication cohort, the 2,351 PD cases had similar demographic distributions, with 70.7% aged over 60 years, 61.8% male, and 94.4% White ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1. The demographic characteristics of participants included in our study. In the discovery cohort, 1,243 drugs were analyzed, with 168 drugs associated with reduced PD risk and 325 drugs linked to increased PD risk after FDR adjustment ( appendix pp 6-22 ). In the replication cohort, 58 drugs were significantly associated with PD risk (22 drugs linked to reduced risk; 36 drugs linked to increased risk) ( appendix pp 23-25 ). Using the drug name replication approach, 18 drugs were successfully replicated (5 associated with reduced PD risk; 13 with increased PD risk) ( figure 1B ; appendix pp 25-28 ). The ATC code replication approach confirmed 15 drugs associated with reduced PD risk ( figure 1C ). Drugs Associated with PD Risk We identified a significant association between salbutamol (β2-adrenergic receptor (β2AR) agonist) and reduced PD risk in both cohorts (discovery: aOR 0.84, 95% CI 0.80-0.88; replication: aOR 0.70, 95% CI 0.49-0.99) ( Table 2 ). In contrast, propranolol, a β2AR antagonist, was associated with an increased PD risk (discovery: aOR 4.15, 95% CI 3.77-4.57; replication: aOR 2.84, 95% CI 1.71-4.97). Two commonly used antihypertensives, amlodipine and losartan, were also linked to a lower PD risk. Notably, of the five drugs related to a lower PD risk, amiodarone was associated with > 20% risk reduction in the discovery cohort and an ∼80% reduction in the replication cohort. Additionally, potassium use, particularly in surgical settings (e.g., gastrointestinal dysfunction), was significantly associated with a lower PD risk. To account for pharmacological similarities among drugs with different generic names but shared therapeutic mechanisms, we used ATC code-based replication and successfully identified 15 drugs across nine drug classes associated with reduced PD risk ( figure 1C ; Table 2 ; appendix pp 2 ). Furthermore, thirteen drugs were associated with increased PD risk and grouped based on their therapeutic indications and relevance to PD ( appendix pp 3 , 26-28 ). In the sensitivity analysis, the associations between most drugs and PD risk remained robust upon stricter exposure definitions and exclusion windows ( figure 1C ; appendix pp 3 , 32-33 ). View this table: View inline View popup Download powerpoint Table 2. The results of 15 repurposed drugs in relation to reduced PD risk. Drugs Associated with PD Progression of Cognitive and Motor Impairment After applying exclusion criteria, 900 PD cases were included for cognitive decline analyses, and 901 PD cases for motor impairment analyses. Nine drugs that remained statistically significant in sensitivity analyses were assessed for progression effects ( appendix p 28 ). After adjusting for covariates, PD patients who had ever used sildenafil or amlodipine exhibited a significantly faster cognitive decline in MoCA scores (sildenafil: β = –0.13, p = 0.019; amlodipine: β = –0.11, p = 0.027) and a significantly faster motor decline by UPDRS III scores (sildenafil: β = 0.83, p = 0.000073; amlodipine: β = 0.66, p = 0.0032), compared to those who had never used these drugs ( appendix p 34 ). Conversely, the use of propranolol (β = 0.18, p = 0.0012) or losartan (β = 0.16, p = 0.016) was significantly associated with improved cognitive function over time ( figure 2A -C ). Moreover, the ever use of solifenacin was associated with a lower rate of motor impairment progression. Download figure Open in new tab Figure 2. Associations between the use of drugs and longitudinal UPDRS III or MoCA score changes in patients with PD. Showing only three drugs (A. Solifenacin; B. Losartan; C. Propranolol) that were significantly associated with delay PD progression of cognitive or motor impairment. The plot showed the relationship between MoCA or UPDRS III score change and time (years), with a linear regression trend line fitted to the data. Separate lines were drawn for ‘never-users’ and ‘ever-users’ of three drugs, respectively. Shaded regions represent 95% CIs. MoCA: Montreal Cognitive Assessment; UPDRS: Unified Parkinson’s Disease Rating Scale; FDR: False Discovery Rate. CI: Confidence Interval. Gene Expression Analysis of Drug Target Genes in PD As a result, a total of 57 protein target genes were identified from the 15 selected drugs using CMap ( appendix pp 28-29 ). To identify novel druggable gene candidates associated with PD, we analyzed the differential expression level of these target genes in a single-cell transcriptomic dataset from the prefrontal cortex of PD patients ( appendix p 29 ). 22 As shown in figure 3 and appendix p 35 , our analysis revealed eight genes that were significantly differentially expressed between PD patients and age- and sex-matched healthy controls, including six downregulated genes ( CFTR, NR3C2, CALM1, DPP4, CACNG1 , and CACNA2D3 ) and two upregulated genes ( CHRM3 and PLA2G4A ). The most significantly altered gene, CFTR , was found to be downregulated in oligodendrocytes ( p = 0.0019, log2FC = –1.0642, figure 3A ). Notably, CFTR expression was regulated by nimodipine in CMap, which also exhibited a mild activation effect on CFTR channel activity. 24 CFTR interacts with key regulators of chaperone-mediated autophagy, including HSPA8, HSP90AA1, and STUB1 ( figure 3C ), highlighting its potential role in this pathway for PD treatment. The other significantly altered gene was nuclear receptor subfamily 3 group C member 2 ( NR3C2, p = 0.0375, log2FC = –1.0379, figure 3B ), which was significantly downregulated in microglia. Dexamethasone, fluticasone and nimodipine were linked to the expression of NR3C2 mRNA. NR3C2 also interacts with HSP90AA1 ( figure 3D ), a key chaperone involved in protein folding. Additionally, NR3C2 is functionally connected to multiple hormone-responsive proteins, including NR3C1, ACE, REN, AGT, PTGES3, HSD11B2, NCOA1, and FKBP4. Download figure Open in new tab Figure 3. CFTR and NR3C2 significantly downregulated in PD. Bubble plot of two differentially expressed genes, CFTR (A) and NR3C2 (B), between PD patients and healthy controls across eight major brain cell types. The color gradient represents the log2 fold change (log2FC), with values centered on white. Dot size corresponds to the log-transformed expression levels of each gene, with individual gene-specific ranges. The x-axis represents –10log10( p ), indicating statistical significance. Neurons (InN, ExN): InN: Interneurons; ExN: Excitatory Neurons. Glia (Oligo, Astro, OPC, MG): Oligo: Oligodendrocytes; Astro: Astrocytes; OPC: Oligodendrocyte Precursor Cells; MG: Microglia. Other cells (T, Endo): T: T cells; Endo: Endothelial Cells. Protein interaction network of (C) CFTR and (D) NR3C2, which retrieve from the STRING database. Candidate genes for this study are bolded and underlined in the figure. Each circle represents a protein, and each line represents an interaction between them. The depth of the line color indicates the strength of the interaction. Discussion In this work, we conducted a retrospective case-control study with a discovery-replication framework, utilizing the MGB Biobank cohort for discovery and the AMP-PD cohort for replication. We aimed to systematically identify drugs significantly associated with PD risk. We identified 18 drugs linked to the risk of later being diagnosed with PD, with five showing marked associations with reduced PD risk. Additionally, 15 drugs were successfully replicated based on their ATC codes. Furthermore, through longitudinal analysis of AMP-PD data, we identified four drugs significantly associated with the progression of cognitive impairment in PD and three drugs linked to motor impairment progression. Finally, we identified two druggable genes as potential therapeutic targets for PD. The consistency of our findings across both cohorts reinforces the reliability of these associations. The association between salbutamol use and reduced PD risk has been previously reported in a comprehensive study integrating experimental data and longitudinal observations. 25 – 27 However, the relationship between β-adrenoceptor agonists and antagonists and PD risk remains debated. Current evidence suggests that this association could stem from true causality, reverse causation, or an indirect relationship. 28 Based on our primary and sensitivity analyses, we support the interpretation of true causality, as we found no evidence of reverse causation or indirect association, even though the smoking history records of participants were not included as a covariate. This finding extends prior observations from our 25 and other labs 29 – 32 showing that β2AR activation robustly reduces SNCA expression via histone 3 lysine 27 acetylation of its promoter and intronic enhancers, normalizing mRNA, protein levels, and aggregates in cellular 25 and rodent models of PD 29 – 31 and in Fabry’s disease 32 . β2AR activation also normalized endogenous α-synuclein protein accumulation in nigral dopamine neurons of a progressive rat model of PD chronically treated with a long-acting, brain-penetrant β2AR ligand. Interestingly, acute 25 and transient 33 effects of β2AR activation on SNCA mRNA expression were observed in wild-type animals, while α-synuclein protein levels in wild-type animals appeared materially unchanged during chronic treatment in one study. 33 Two antihypertensive drugs commonly prescribed to PD patients, amlodipine and losartan, have also been previously studied for PD therapeutic potential in retrospective cohort analyses and experimental models. 34 – 38 While losartan has been suggested to exert neuroprotective effects through modulation of the renin-angiotensin system (RAS), findings on amlodipine remain inconclusive. Furthermore, amiodarone, an antiarrhythmic drug, was also found to be significantly associated with a reduced PD risk in our analyses. While its direct role in PD remains unexplored, some studies suggest that amiodarone may influence immune responses 39 , 40 and autophagic pathways. 41 , 42 Given the role of neuroinflammation and α-synuclein protein clearance in PD, future research is needed to determine whether amiodarone could modulate these processes and perform neuroprotective effects. Potassium plays a crucial role in neuronal excitability and mitochondrial function, both implicated in PD pathophysiology. 43 However, in our analysis, potassium-related medications, including potassium chloride, were primarily associated with perioperative or supportive care, rather than long-term therapeutic use. Thus, the observed association with PD risk may reflect underlying clinical conditions rather than a direct neuroprotective effect. In our study, we originally identified five drugs associated with longitudinal changes in cognitive and motor impairment in PD. Notably, we found that losartan was significantly associated with a slower rate of cognitive decline in PD patients. At present, the RAS has been increasingly recognized as a critical target for the pathophysiology understanding of PD. 44 , 45 Evidence have suggested that RAS dysregulation contributes to neurodegeneration by promoting neuroinflammation, oxidative stress, and cerebrovascular dysfunction. 46 , 47 In particular, the activation of the angiotensin type 1 (AT1) receptor has been implicated in exacerbating α-synuclein aggregation and its transmission between neurons and glial cells, thereby accelerating PD progression. 48 Among RAS-targeting therapies, ARBs and angiotensin-converting enzyme inhibitors (ACEis) are the two main pharmacological classes. As an ARB, losartan has been associated with neuroprotective effects in preclinical PD mouse model, 38 where it mitigated cognitive decline via RAS modulation, reducing neuroinflammation and improving cerebral perfusion. 48 Importantly, our findings highlight a novel insight of ARB-mediated neuroprotection benefits for PD patients. While previous epidemiologic studies have shown protective effects of ARB in PD, 49 , 50 our results specifically underscore the potential of losartan in both prevention (reducing PD risk in 0–5 years after initiation of prescriptions) and progression (preserving cognition in PD patients over time). Given these promising findings, ARBs such as losartan and telmisartan 51 urgently need further clinical investigation to explore their benefits in PD patients, particularly in mitigating cognitive impairment. Moreover, our study found that PD patients with solifenacin use exhibited a significantly slower rate of motor impairment. Solifenacin is a competitive muscarinic receptor antagonist for overactive bladder (OAB) symptoms treatment. 52 Given its anticholinergic properties, this effect is likely symptomatic rather than disease-modifying in PD. Anticholinergic drugs, such as trihexyphenidyl, are previously used to alleviate motor symptoms in PD, 53 suggesting that solifenacin may confer similar benefits. The link between propranolol, a non-selective βAR-blocker and PD is complex. Propranolol is widely used to treat tremor and has been shown tremor-reducing effects on the motor cortex of PD in a recent placebo-controlled crossover trial study. 54 Associations may be due to isolated tremor during early motor stages of individuals on the way to developing PD. Mechanistically, βAR-blockers are linked to increased SNCA expression in cells. 25 Our study performed an unexpected association with cognitive benefits. These diverse epidemiologic and biological signals will require more focused, well-controlled experimental studies to clarify and disentangle putative symptomatic, molecular, prodromal, and clinical effects. In addition to the core findings presented above, we also observed noteworthy associations between several medications commonly prescribed for prodromal non-motor symptoms (e.g., depression, bladder dysfunction, orthostatic hypotension) and increased PD risk. These associations suggest that a data science-based biomarker for prodromal PD detection could be developed using patterns of non-motor symptom drug use. Identifying individuals at early, prodromal disease stages remains a major challenge but is crucial for early intervention and recruitment into disease-modifying secondary prevention trials. Moreover, through drug-target screening and single-cell transcriptomic integration, we identified downregulated genes in PD (notably CFTR and NR3C2 ) with potential relevance to disease pathophysiology and therapeutic targeting. Finally, leveraging multi-cohort data and ATC-based drug classification, we highlighted advantages of our study to perform robust drug repurposing analyses. These findings are elaborated in the appendix (pp 3-4) . Our study has several limitations. First, the study is observational in nature, meaning that causality cannot be definitively established. Although PSM was applied to adjust for confounding variables, residual confounding may still affect the results, particularly regarding socioeconomic factors and other unavailable variables, such as smoking history and common comorbidities. Second, the reliance on retrospective data, while large, may introduce biases related to data completeness and accuracy. Although the replication cohort included many individuals, the results may not be fully generalizable to all PD populations, particularly those from non-Western countries or with different healthcare access. Third, while single-cell transcriptomic analysis provides valuable insights, the study did not investigate the mechanistic pathways in detail, which warrants further experimental studies. Fourth, although the study identified several drugs associated with PD risk and progression, prospective clinical trials are necessary to validate the therapeutic potential of these repurposed drugs. In conclusion, our study identifies several drugs associated with PD risk and progression, as well as potential druggable targets for therapeutic intervention. Future studies should investigate the underlying mechanisms of these drugs in larger cohorts and assess their efficacy in randomized controlled trials to determine their potential role in altering PD progression. Data Availability All the data and scripts can be downloaded in GitHub ( https://github.com/TheDongLab/ReRx ). The raw data are available upon reasonable request, following approval by the MGB Biobank and AMP-PD (PPMI and PDBP). https://github.com/TheDongLab/ReRx https://zenodo.org/records/15360577 Contributors Xianjun Dong and Clemens R. Scherzer co-designed the study. Yuxuan Hu performed medication data collection, preprocessing, and statistics analyses, where Miles Waits helped to clean up and provide more detailed medication data. Weiqiang Liu and Le Zhang conducted the single-cell RNA-seq data analysis. Yuxuan Hu, Weiqiang Liu and Xianjun Dong prepared tables and figures. Yuxuan Hu and Weiqiang Liu wrote the first draft of the manuscript. All authors contributed to and edited the manuscript. All authors read and approved the final version of the manuscript. Declaration of Interests All authors declare no competing interests. Data Sharing All the scripts can be downloaded in GitHub ( https://github.com/TheDongLab/ReRx ). The raw data are available upon reasonable request, following approval by the MGB Biobank and AMP-PD (PPMI and PDBP). Acknowledgments Data used in the preparation of this article were obtained from the Accelerating Medicine Partnership® (AMP®) Parkinson’s Disease (AMP PD) Knowledge Platform. For up-to-date information on the study, visit https://www.amp-pd.org . The AMP® PD program is a public-private partnership managed by the Foundation for the National Institutes of Health and funded by the National Institute of Neurological Disorders and Stroke (NINDS) in partnership with the Aligning Science Across Parkinson’s (ASAP) initiative; Celgene Corporation, a subsidiary of Bristol-Myers Squibb Company; GlaxoSmithKline plc (GSK); The Michael J. Fox Foundation for Parkinson’s Research ; Pfizer Inc.; AbbVie Inc.; Sanofi US Services Inc.; and Verily Life Sciences. ACCELERATING MEDICINES PARTNERSHIP and AMP are registered service marks of the U.S. Department of Health and Human Services. We thank the AMP PD consortium and contributing cohorts for their data contributions. The Parkinson’s Progression Markers Initiative (PPMI) is sponsored by The Michael J. Fox Foundation for Parkinson’s Research and supported by a consortium of scientific partners: 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson’s, AskBio, Avid Radiopharmaceuticals, BIAL, BioArctic, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol-Myers Squibb, Calico Labs, Capsida Biotherapeutics, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Jazz Pharmaceuticals, Johnson & Johnson Innovative Medicine, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Neuron23, Neuropore, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager Therapeutics, the Weston Family Foundation and Yumanity Therapeutics. The PPMI investigators have not participated in reviewing the data analysis or content of the manuscript. For up-to-date information on the study, visit www.ppmi-info.org . The Parkinson’s Disease Biomarker Program (PDBP) consortium is supported by the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health. A full list of PDBP investigators can be found at https://pdbp.ninds.nih.gov/policy . The PDBP investigators have not participated in reviewing the data analysis or content of the manuscript. We acknowledge the Mass General Brigham (MGB) Biobank and its participants for providing samples, genomic data, and health information data. This research was funded in whole or in part by Aligning Science Across Parkinson’s [ASAP-000301] and [ASAP-000529] through the Michael J. Fox Foundation for Parkinson’s Research (MJFF). For the purpose of open access, the author has applied a CC BY public copyright license to all Author Accepted Manuscripts arising from this submission. Yuxuan Hu is also supported by the Exploration World Program from China Pharmaceutical University for his first year in Brigham and Women’s Hospital. References ↵ Dorsey ER , Bloem BR . The Parkinson Pandemic-A Call to Action . JAMA Neurol 2018 ; 75 : 9 – 10 . OpenUrl PubMed ↵ Aarsland D , Batzu L , Halliday GM , et al. Parkinson disease-associated cognitive impairment . Nat Rev Dis Primers 2021 ; 7 : 47 . 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